Report generated on 2019-10-27, 12:13 based on data in:
/mnt/research/ShadeLab/GLBRC/mapping/metaG/fullAssembly/trimStats
Trimmomatic is a flexible read trimming tool for Illumina NGS data.
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from IPython.core.display import HTML

Method: Reads were trimmed, aligned to the host assembly, and reads with neither pair aligninging were extracted and aligned to the collection of fungal assemblies. All reads with neither pair aligned to the fungal assemblies were extracted again in "cleaned" fastq files. MegaHit was then used with the "--kmin-1pass" and "--presets meta-large" options to generate a metagenomic assembly.MetaT Sequencing and alignment Summaries
Next steps:
In order for this to progress, 1 had to be complete. The next step is figure out why the metadata for the MetaT is not meshing with the R script. Nejcs was OOO last week but said that we could meet up this week.
Kallisto has an arguement to export a bam file when it runs, but I do not know how these bam files work or if they are the same thing as a bam from bowtie2. I am working with kallisto to get a bam file now and then I will see if that file works to extract annotations from the contigs
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Step 1. Split JGI files in PE files
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# <!-- # #HIDDEN
# # tImages = {"MetaG":Image("images/TrimReport_MetaG.png"),"MetaT":Image("images/MetaT_trimmomatic_plot.png")}
# # @interact
# # def trimReport(Source=["MetaG","MetaT"]):return tImages[Source]
# <img src="images/TrimReport_MetaG.png"></img>
# -->
HTML("html/MetaG_TrimStats.html")
Step 1. Align reads to respective host reference assembly
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HTML(filename="html/BothPerc.html")
Step 1. Extract reads that don't align the plant assembly
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HTML(filename='html/FungalAlign.html')
Method: Reads were trimmed, aligned to the host assembly, and reads with neither pair aligninging were extracted and aligned to the collection of fungal assemblies. All reads with neither pair aligned to the fungal assemblies were extracted again in "cleaned" fastq files. MegaHit was then used with the "--kmin-1pass" and "--presets meta-large" options to generate a metagenomic assembly.
The annotations were performed using KEGG's prokaryotic peptides and eukaryotic peptides

Based on the high-overlap, we decided to use the mags instead of the assembly for alignment
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HTML(filename="html/MAG_Stats.html")
MetaG Analysis¶
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Call: adonis(formula = dist.otu ~ map_16S\$time_numeric)
Permutation: free Number of permutations: 999
Terms added sequentially (first to last)
| Df | SumsOfSqs | MeanSqs | F.Model | R2 | Pr(>F) | |
|---|---|---|---|---|---|---|
| map_16S$time_numeric | 1 | 1.9356 | 1.93562 | 47.814 | 0.26298 | 0.001 *** |
| Residuals | 134 | 5.4246 | 0.04048 | 0.73702 | ||
| Total | 135 | 7.3603 | 1.000000 |


Call: adonis(formula = dist.otu ~ map_16S\$treatment)
Permutation: free Number of permutations: 999
Terms added sequentially (first to last)
| Df | SumsOfSqs | MeanSqs | F.Model | R2 | Pr(>F) | |
|---|---|---|---|---|---|---|
| map_16S$treatment | 1 | 0.0289 | 0.028930 | 0.52878 | 0.00393 | 0.755 |
| Residuals | 134 | 7.3313 | 0.054711 | 0.99607 | ||
| Total | 135 | 7.3603 | 1.000000 |
Call: adonis(formula = dist.otu ~ map_16S\$plant)
Permutation: free Number of permutations: 999
Terms added sequentially (first to last)
| Df | SumsOfSqs | MeanSqs | F.Model | R2 | Pr(>F) | |
|---|---|---|---|---|---|---|
| map_16S$plant | 1 | 0.1617 | 0.161704 | 3.0101 | 0.02197 | 0.027 * |
| Residuals | 134 | 7.1986 | 0.053721 | 0.97803 | ||
| Total | 135 | 7.3603 | 1.000000 |
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HTML("html/MetaT_TrimStats.html")

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HTML("html/MetaT_HostAlignment.html")

MetaT Analysis¶All bins with at least 50% completeness were used in a combined assembly. The analysis below comes from the counts for each sample. These counts were normalized by the number of reads that did not align to the fungal or host assemblies.
HTML("html/MetaT_MAG_Align.html")